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Enhance & Explore: CH-1015 Lausanne adel.aziz@epfl.ch http://people.epfl.ch/adel.aziz Adel Aziz joint work with Julien Herzen, Ruben Merz, Seva Shneer, and Patrick Thiran September 21 st 2011 LCA an Adaptive Algorithm to Maximize the Utility of Wireless Networks
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Objective Maximize given utility function Challenges vs. related work Work on existing MAC No network-wide message passing Wireless capacity is unknown a priori 1/14 Problem Statement (Max-Weight based [1] ) (IFA [2] ) (GAP [3] ) [1] Proutière et al., CISS, 2008 [2] Gambiroza et al., MobiCom, 2004 [3] Mancuso et al., Infocom, 2010
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WLAN Setting Inter-flow problem Optimally allocate resources Multi-Hop Setting Intra-flow problem Avoid congestion 2/14 Motivation [3] Heusse et al., Infocom, 2003 [3]
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Flow = No scaling problem Architecture of node j Information to set at node j at time slot n Rate allocation vector: Information to monitor at GW at time slot n Measured throughput: 3/14 Network Abstraction
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Useful information at time slot n Measured throughput: Capacity region: (unknown a priori) Rate allocation vector: Last stable allocation: Utility function: Level set: 4/14 Network Abstraction x1x1 X*X* x2x2
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Starts from IEEE 802.11 allocation oThroughput vector: x[0] = ρ[0] {rate allocation} oLevel set of utility μ[0]: L(x[0], μ[0]) oRemember allocation:r[0] = x[0] Enhance phase: (Find the next targeted level set) oIf (x[n-1] = ρ[n-1] ) μ[n] = full-size gradient ascent oElse μ[n] = halved-size gradient ascent Explore phase: (Find the next allocation) oPick point ρ[n] randomly oIf (x[n] = ρ[n] ) Remember new allocation: r[n] = x[n] Go to Enhance phase oElse Repeat Explore phase at most N times, then move to Enhance μ[0] μ[2] μ[3] μ [1] Truncated Gaussian pdf μ [4] Example: N = 2 ‘Enhance & Explore’ in WLAN 5/14
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Theorem for E&E Utility of never decreases through time converges to an allocation of maximal utility Converges for any initial rate allocation Assumptions for the proof Fixed capacity region Coordinate-convex capacity region Much weaker than convexity! Future work Study speed of convergence 6/14 Optimality Theorem
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GW E&E decides the per-flow rate allocation One-hop nodes E&E rate-limits one-hop nodes Multi-hop nodes EZ-flow [1] rate-limits multi-hop nodes 7/14 Complete Solution: E&E + EZ [1] Aziz et al., CoNEXT 2009
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Based on Click [1] with MultiflowDispatcher [2] Creation of 5 new elements MFQueue MFLeakyBucket EEscheduler EEadapter EZFlow Evaluation with Asus routers Ns-3 [2] Schiöberg et al., SyClick, 2009 8/14 Practical Implementation [1] Kohler et al., Transactions on Computer Systems, 2000
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Deployment map: Without E&E: With E&E: (proportional fairness) 9/14 Experimental Results in WLAN
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Inter-flow fairness Appropriate queuing is needed (Fair Queuing [1] ) … but it is not enough 10/14 [1] Demers et al., Sigcomm’89 Starvation in Mesh Networks
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Deployment map: Without E&E: With E&E: (proportional fairness) 11/14 Practical Results in Mesh Networks 1-hop flow 3-hop flow 1-hop flow 3-hop flow
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Ns-3 simulator Re-use of same Click elements More controlled environment Possible estimation of capacity Computation of optimum 12/14 Simulation Results in WLAN
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Include downstream traffic Study and improve the speed of convergence Analyze new distributions for the Explore phase Study the interaction with rate adaptation 13/14 Future Work
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Wireless networks suffer from Intra-flow problem (e.g., congestion) Inter-flow problem (e.g., unfairness) Time-variability Difficulty/impossibility characterizing the capacity region Need adaptive algorithms to maximize a desired utility E&E solves the inter-flow problem in WLAN Combining E&E and EZ-Flow in mesh networks Solves both the inter-flow and intra-flow problem Avoids network-wide message passing Does not modify networking stack 14/14 Conclusion
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